Comparative Study of LQR, LQG and PI Controller Based on Genetic Algorithm Optimization for Buck Converters

Almaged, Mohammed; Khather, Salam Ibrahim; Abdulla, Abdulla I.; Amjed, M.

doi:10.23919/eleco47770.2019.8990572

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the methods used to solve optimization problems with uncertain parameters is the linear quadratic gaussian (LQG) [12]. There are several reports in varying fields regarding the advantages of using this control method: for example, power system [13], [14], mechanical control problem [15], [16], power flow [17], unmanned aerial vehicle [18], simplified car [19], acquisition process [20], slung transportation [21], vehicle automation [22], [23], buck converter controlling [24], robotic & electronic systems [25], [26], and optics system [27].…”

Section: Introductionmentioning

confidence: 99%

Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method

Sutrisno

Widowati

Tjahjana

2021

IJEECS

View full text Add to dashboard Cite

This study formulates a dynamical system for the control of a single product inventory system in accordance with the random value of demand and the percentage of damaged product during the delivery process. The formulated model has the form of a linear state-space system comprising of two disturbances, which represents the random value of demand and the percentage of the damaged product during delivery. The optimal value of the product amount ordered to the supplier is properly calculated by using the linear quadratic gaussian (LQG) method. The controller is used by the manager to make inventory level decisions under the uncertainty of demand and damaged items during the product delivery process. The result showed that the optimal product order for each review time was achieved, and the inventory level was used to obtain the right set point properly. Moreover, based on comparison with other research results, the proposed model was well performed.

show abstract

Section: Introductionmentioning

confidence: 99%

Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method

Sutrisno

Widowati

Tjahjana

2021

IJEECS

View full text Add to dashboard Cite

show abstract

Design and comparison of particle swarm optimization tuned Kalman filter based linear quadratic Gaussian controller and linear quadratic regulator for surface to air missile guidance system

Alitasb,

Beyene,

Salau

2024

Adv Control Appl

View full text Add to dashboard Cite

The study of missile guidance systems is a well‐known nonlinear control engineering area of research. To enhance the control performance of a missle guidance system, several technologies have been proposed in existing works. To resolve the weighting matrix selection issue of a linear quadratic Gaussian (LQG) controller for the surface‐to‐air missile guidance control system, this study utilizes the particle swarm optimization (PSO) technique. Selecting the best state (Q) and input (R) weighting matrices is a significant difficulty in the design of the LQG controller for real‐time applications since it affects the controller's performance and optimality. The weighting matrices are often chosen by a trial‐and‐error method that not only complicates the design but also does not yield optimal outcomes. Therefore, in this paper, a PSO method is developed and used in the design of the linear quadratic regulator (LQR) and LQG controllers for the surface‐to‐air missile control system to choose the elements of the Q and R matrices in the best possible way. Finally, a comparative analysis between the designed controllers was presented. The results shows that a good performance was achieved by using the proposed PSO‐tuned design process. The LQG and LQR are designed by manually adjusting the weighting matrices and utilizing an intelligent procedure, PSO algorithm which achieved optimal results. Further results indicate that the designed controllers, the PSO tuned LQR and LQG achieved a better performance over the manually adjusted LQR and LQG controllers.

show abstract

Antenna Azimuth Position Control Using Fractional Order PID Controller Based on Genetic Algorithm

Mahmood

Almaged

Abdulla

2021

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

This work shows the design procedure of fractional-order proportional–integral-derivative (FOPID) controllers for an antenna azimuth position system based on genetic algorithm optimization approach. FOPID controllers are considered as a special type of the classical PID controller in which the derivative and integral elements have orders of fractions between zero and one. Therefore, FOPID controllers comprise two additional variables ( μ and λ ) in comparison with the typical PID controller. The FOPID is designed to control the azimuth angle of the antenna. Genetic algorithm (GA) will be employed to find an optimal value for the five FOPID controller’s gains using different type of fitness function including mean square error (MSE), integral square error (ISE) and integral time square error (ITSE). The achieved results exhibit an excellent steady and transient state performance of system, for instant fast settling and rise times as well as low present of peak overshoot.

show abstract

Comparative Study of LQR, LQG and PI Controller Based on Genetic Algorithm Optimization for Buck Converters

Cited by 3 publications

References 23 publications

Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method

Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method

Design and comparison of particle swarm optimization tuned Kalman filter based linear quadratic Gaussian controller and linear quadratic regulator for surface to air missile guidance system

Antenna Azimuth Position Control Using Fractional Order PID Controller Based on Genetic Algorithm

Contact Info

Product

Resources

About